In the European Union trucks, busses and coaches produce around 25 % of the CO2-emissions from road transport. Because of increased road freight traffic, the CO2-emissions rose by 36 % between 1990 and 2010. Therefore, it is planned to also limit and certify the CO2-emissions of new heavy duty vehicles (HDV). One solution to reduce the CO2-emissions of a HDV is to electrify the powertrain. Depending on the driving cycle, different fuel consumption improvements can be achieved with electrification of the powertrain. For a series hybrid bus, up to 33 % of improvement has already been demonstrated. For the fleet operator the potential fuel savings that can be achieved with an alternative powertrain on a daily route is crucial for the selection of the right type of vehicle. For the vehicle manufacturer knowledge about the expected driving cycles is important to size the powertrain parameters appropriate. Hence, different aspects have to be considered when designing hybrid powertrains. To exemplify an advanced development approach, considering the requirements of, both, the operator and the OEM to find a best cost/benefit solution, this paper demonstrates the optimization of an electric powertrain for a hybrid electric distribution vehicle. All components of the electrical powertrain, like high-voltage battery and electric machine are scalable in the simulation model. By means of design of experiments, not only a best compromise between fuel consumption and cost will be determined, but also the requested vehicle performance targets like acceleration from 0 to 50 km/h and climbing capacity will be taken into account. The simulation study will compare the results of a conventional powertrain on at least two different typical routes with a series hybrid and parallel hybrid powertrain.

A securely connected self-drive car aims to reduce its global CO2 footprint. At the moment, a lot of efforts are put in by the automotive industry to manufacture cars for a greener environment. Besides, Tesla’s breakthrough of elegant EVs is motivating vehicle OEMs like BMW, Volkswagen, Nissan, and others to step more rapidly into the EV business. According to IHS, fuel-efficient technologies and increased electrification will help car OEMs to facilitate environmentally friendly cars. Nowadays, a substantial amount of work is done on the exhaust aftertreatment systems to improve the fuel efficiency. For internal combustion engines, there is an increasing trend away from traditional incumbent multi-port fuel injection systems towards gasoline direct injection systems. This is simply because direct injection system increases the fuel efficiency of a vehicle. The prevailing concepts in the engine and the exhaust aftertreatment systems for internal combustion engines (ICEs) require sensors for their operation. The increase in sensor content will have ECUs with more inputs and this will push the need for intelligent controllers with higher speed operation and embedded memory. According to IHS, start-stop systems or so called micro-hybrids will outset the development of vehicle electrification on a large scale. Such a system, in which the engine turns itself off when the car stops at a junction or stop light, can improve the fuel efficiency up to 10%. This technology is already widely adopted by the European market but will also gain momentum in North America and Asia as each recognizes the importance of fuel efficiency and lower CO2 emissions. Increased vehicle electrification requires an alternative to the gasoline technology for driving the engine with the same amount of power. Thus, IHS sees 48V board-net bus technology as the next milestone to enable green car. This technology will allow a car to have functions such as recuperation, boosting, and sailing. A function like boosting would be achieved by the combination of electric motors working in tandem with the conventional engine. For this reason, the 48V technology would typically require three ECUs: a motor inverter for converting AC into DC and vice-versa, a DC/DC converter for bi-directionality, and a battery management system for supplying the 48V to the bus. Propulsion systems for electric and hybrid vehicles demand, on average, ten times more semiconductor content than a conventional engine. DC/DC converters for this technology would have to comply with ASIL-C requirement. In order to meet ASIL-C, as many as 18 MOSFETs would be needed to act as safety switches. With the rising levels of voltage levels on batteries for HEV/EVs, switching the voltage levels becomes a critical task for the power management components. MOSFETs, IGBTs, and diodes are growing in number to perform the switching functions. The high and low side driver ICs are needed to drive currents for the relays, injectors, and the different valves. Eventually, IHS perceives the battery management solutions to have a significant bearing in the cost of HEV/EVs. For instance, high-end EV brands like Tesla house around 7000 cells as opposed to Nissan Leaf which typically has around 192 cells. The presentation will cover the topics described above and its impact on the powertrain semiconductor and system level. It will talk about the developments and factors which are driving the changes within the propulsion system designs. It will also highlight how the electronics are powering the hybrid/electric and fuel-efficient vehicles.Last but not the least, the evolution in electronics which is enabling innovative architectural changes in powertrain of hybrid and electric vehicles will be discussed as well.

This paper evaluates the benefits of replacing the Si diodes of a commercial IGBT module for the main inverter application of an electric vehicle with SiC diodes, leaving the other components of the package and the system unchanged. This will give a direct comparison of Si vs SiC, without giving scope for discrepancies arising out of differences in the packaging, gate-driver circuit etc. The IGBT-module chosen for comparison is a low inductance (8nH) HybridPACK Drive module from Infineon, suitable for very high speed switching. The focus of the comparison is the following:

Static and dynamic characterization of the Si and hybrid-SiC modules.

Development of a loss model for the modules based on the above measurements.

Application of the developed loss model to investigate the performance for various drive cycles (Artemis, WLTP, NEDC).

Development of a novel calorimetric loss measurement setup for experimental verification of the results.

Motivation Papers that address the topic of SiC for automotive main inverters often have one or more of the following drawbacks: The considered devices are rated for low currents, quite far from the typical application requirements of the main inverter The devices/packages chosen are prototypes, which do not face the same constraints a mass product would. The compared Si and SiC chips are in completely different packages or application conditions In short, a clear investigation of the benefits of using SiC as a plug-and-play replacement for a commercial Si-IGBT module is still missing in literature. This gap is attempted to be filled by the paper Results Fig. 1 shows an IGBT-diode pair of the compared HybridPack Drive module with Si diodes (the module will be referred to as “HPD”) and SiC diodes (the module will be referred to as “HPD-Hyb-SiC”) respectively. Static losses are measured with a curve tracer and the switching energies of both the modules are measured in a double pulse test setup (see Fig. 3). The SiC diodes help reduce E_on and E_rec by around 25% and 75% respectively. A loss model is derived based on these measurements and this model is used to calculate the inverter losses for Artemis, NEDC and WLTP drive cycles. As seen in Fig. 3, HPD-Hyb-SiC offers more than 10% reduction in losses for all the drive cycles. The Artemis Urban cycle with a reduction of around 20% losses sees the most benefit of using SiC diodes. Also, the inverter losses at different rms currents and dc-link voltages are measured in a novel calorimetric setup (see Fig. 2) introduced in the paper and the results are summarized in Fig. 3. It can be seen that there is no benefit of using the SiC diodes at V_dc=100V. At V_dc=300V, the benefits of SiC become prominent and this gap widens as we increase V_dc, and at V_dc=400V, 75A, we can see a reduction of around 7% in the overall losses. Also, the simulated losses are found to be in good agreement with the measured losses, thereby validating the loss model.

Despite the battery cells itself, the electronics, especially sensors make about 10 % of the system costs and volume in automotive applications. As the cell price and volume is decreasing, the amount of cells will increase within a Battery Electric Vehicle (BEV). Accordingly the number of monitored cells increases as there is often a 100 % monitoring rate in regard to li-ion battery systems (BS) due to safety reasons. This means that each cell is employed with a voltage sensor and each stack with a current sensor, or if a balancing system is employed, there might be an additional current sensor for each cell. With a stack as big as 12 cells, this means 13 or even 24 (if a balancing system is employed) sensors per stack. The number of sensors increases when taking temperature sensors into account. The newly developed sensor minimal battery observer allows decreasing this number of voltage and current sensors by 90 % down to two sensors per stack by keeping up the 100 % monitoring rate. This means that the presented system affords only two sensors to monitor 12 battery cells individually. This remarkable decrease of the number of sensors leads to a commensurate decrease of costs, weight and volume. Despite the reduction of sensors, it means a decrease of cables and assembling as well as construction costs. In addition to the sensor minimal observer system the system includes a cell balancing opportunity, which makes additional systems abdicable. This balancing system ensures an equivalent energy level in all associated cells to allow maximal energy output and extended service live. The service live can be extended to five times the service live of an unbalanced system for automotive application (end of life capacity 80 %). Concomitant the traction range is increased well after a few recharges due to the application of balancing systems.

BATman is a low power Battery Managment System, which calculates important battery values like state of health, state of charge and state of function. BATman provides the user the opportunity of monitoring the state of the cars battery during the runtime. The measurement parameters voltage and current for calculation are detected by an AS8510 measurement device, which communicates with an Atmel AT32UC3C microcontroller unit (MCU) via SPI. It gets the data with a frequency of 8 kHz for each value. To determine the values a shunt resistor is connected to the battery. The third parameter is the temperature, which is measured with an internal analog digital converter of the MCU. The ADC scales the voltage on a PT100 resistor. The MCU calculates the battery values after getting an interrupt from the measurement device. There are different ways of calculation for the different parameters. The SOC is determined with coulomb counting on one side and about the neutralvoltage on the other side. The SOH is calculated with the internal resistance of the battery. The values are stored in two different sorts of memories. The first memory is a SD-Card for storing data for the user in short time intervals. The second memory is an EEPROM. The EEPROM protects the software parameters in case of supply interruption. The microcontroller communicates with the SD-Card via SPI, too. The communication between EEPROM and controller comes about I2C. The user can access the battery data by connecting to BATman with Bluetooth. The user can set a real time clock which is supported by a 32 kHz oscillator. The Bluetooth IC works as data pump like a serial interface and it is connected with SPI to the microcontroller. For communication with other devices in automotive surrounding BATman includes a CAN communication unit, and allows the integration of LIN as well. An implemented USB socket allows a simplified programing of the MCU with a computer.

Electromobility is a promising way of locomotion for eco-conscious, future-oriented users. The popularity of electrically powered vehicles increases. Manufacturers offer innovative concepts and promise their customers to reduce the operation and energy costs by buying their products. However, it is difficult to find your way in the growing but still limited supply of vehicles and drive concepts. The Smartphone application described in this article is aimed at those people who have an interest in purchasing an electric or hybrid vehicle, but are not able to verify themselves which kind of car on the market fits to their needs. The application is to help them take their user-specific handling characteristics, to evaluate and to provide an appropriate overview of vehicles from different manufacturers on this basis. The data is recorded during a trip with a conventionally powered vehicle. The app uses the built-in smart sensors and interfaces such as GPS and accelerometer. The data is collected over an individually selected period of time during each trip and allows conclusions on the driving behavior of the user. A recorded track includes altitude, acceleration and velocity profiles which help to estimate the individual energy consumption of each track. By optionally entering charging facilities at the end of each trip it is stated weather and how the vehicle can be charged. After of using the App for several days or even longer, the analysis processes of the data can be performed. The result given is an overview of various electric and hybrid vehicles, which fulfill the energy requirements of previously recorded tracks. The algorithms of the program do not only consider the details of the manufacturer and the declared range by NEDC but also consider inclines, the velocities and the acceleration behavior of the user, which have an impact on the energy demand and thus the range of the vehicle. Thus, the application provides a decision aid, which does not rely only on the static comparison of estimated values, but rely on real measurements and therefore offer individual results.